2024 Past Paper PDF - Epidemiology

The end Russell Jude de Souza Togo Salmon Hall Room 130 Mark breakdown 72 multiple choice questions (of which are 2 bonus questions) 150 minutes (2 h, 30 m) The denominator is 70 (this means you can get 2 questions wrong and still score 100%) 30 application questions 29 definit...

The end Russell Jude de Souza Togo Salmon Hall Room 130 Mark breakdown 72 multiple choice questions (of which are 2 bonus questions) 150 minutes (2 h, 30 m) The denominator is 70 (this means you can get 2 questions wrong and still score 100%) 30 application questions 29 definition questions 13 calculation questions Types of questions Application questions: Take information that you've learned in the course and use it to choose the best response from the available options (e.g., classify bias, recognize study design) Definition questions: Identify the best definition of the concept, formula, or study design described. Calculation questions: Do the math using the appropriate formula. Topics to study Topic # questions Topic # questions HTA 11 Indigenous Cultural Safety 4 Bias 8 Confounding/EM 2 Genetic epidemiology 7 DTA 2 Causal inference 6 Fundamentals 2 Observational studies 6 ID Epi 2 Health Measures 5 RCT 2 Nutritional epidemiology 5 Systematic reviews 1 Screening 5 Metabolomic epidemiology 1 Know these calcuations Incidence and prevalence Case fatality rate Proportionate mortality Sensitivity and specificity Incremental cost-effectiveness ratio Direct and indirect vaccine efficacy Attributable risk What is Epidemiology? The word epidemiology comes from the Greek words epi, meaning “on” or “upon”, demos, meaning “people”, and logos, meaning “the study of” MacMahon and Pugh (1970): “The study of the distribution and determinants of disease frequency in man” Last (1983): “The study of the distribution and determinants of health- related states and events in populations, and the applications of this study to control of health problems” 6 Intervention questions Population Intervention May be applied to intervention studies, etiologic studies, and Comparator diagnostic and screening studies. Outcome(s) Time Setting 7 Exposure questions Population Exposure May be applied to intervention studies, etiologic studies, and Comparator diagnostic and screening studies. Outcome(s) Time Setting 8 Incidence The number of affected persons present in the population at a specified time divided by the number of persons in the population at that time Point Prevalence (one point in time) Period Prevalence (over a period of time) 9 Incidence Number of new cases of disease in population in period of time / # people at risk in same population and time period 10 Incidence proportion: Example We would like to estimate the incidence proportion of type 2 diabetes in a nursing home with 800 residents from Jan 1 to Dec. 31, 2022 11 Incidence proportion: Example On Jan. 1, 50 of them already have diabetes (“prevalent”) not at risk of developing new diabetes during the observation period Leaves 750 “at risk” of developing over 12-months (800 – 50) 25 develop diabetes over that 12-months What is the “incidence proportion”? 12 Incidence proportion: Examples On Jan. 1, 50 of them already have diabetes (“prevalent”) not at risk of developing new diabetes during the observation period Leaves 750 “at risk” of developing over 12-months (800 – 50) 25 develop diabetes over that 12-months What is the “incidence proportion”? 25/750 = 0.033 or 3.3% 13 Quantifying death Mortality rate: number of deaths occurring in a population during a particular period of time Proportionate mortality: percentage of deaths occurring in a population during a specific period of time from a specific cause Case fatality rate: percentage of people who contract a particular condition who die from it 14 Case Fatality Rate % of people with a particular disease who die from that disease As of September 12, 2023, Canada had reported 4,716,000 cases of COVID- 19 (since beginning) As of September 12, 2023, Canada reported 53,541 deaths What is the case fatality rate of COVID-19 in Canada? 15 Calculating Case Fatality Rate 53,541 4,716,000 = 0.0114 = 1.14% “On average, 1.14% of people in Canada who contract COVID-19 will die from it.” 16 Proportionate Mortality Total mortality 2016: 740/100,000 people/year Stroke mortality 2016: 39.2/100,000 people/year What was the proportionate mortality from stroke? 17 Calculating Proportionate Mortality Total mortality 2016: 740/100,000 people/year Stroke mortality 2016: 39.2/100,000 people/year Proportionate mortality from stroke= 39.2 𝑥 100 = 5.3% 740 18 Observational Study Designs Observational studies are often called “natural experiments” the investigator does not manipulate treatment or exposure to disease, but rather observes natural variation 19 Case Reports Consist of one person (patient) Typically describe unusual symptoms or syndromes observed in this one person May contain speculation about exposure 20  Case Reports Strengths Limitations Identify unusual findings Case may not be generalizable Describe course in hospital – Not systematic – a “unique” case: others may present differently, require different treatment Causes or associations may have other explanations – May have a rare form of respirator illness 21 Case Series A group or series of case reports involving patients with a specific presentation who were given similar treatment Usually contain detailed information about the individual patients Demographic information, (age, gender, ethnic origin) Diagnosis, treatment, response to treatment, and follow-up after treatment Defined protocol and inclusion/exclusion criteria 22 Case Series Strengths Limitations Same reasons as case reports; but with more If the series is retrospective, it will depend on people! the availability and accuracy of the data records Help to identify, or “characterize” rare conditions or treatment courses Subject to selection bias because the clinician COVID-19: mortality was lower than previous or researcher self-selects the cases studies, despite comparable patient characteristics (26-80%) The findings reported may not be generalizable (i.e., apply to the population) Due to a broader system-level response that It is often impossible to know what would have prevented surge of critically ill patients with happened to the “Cases” if they had not been COVID-19 from presenting --- 40% of COVID treated patients had an ICU stay 23 Relationship between Exposure and Outcome Exposure Outcome Something we: Something that happens to us: Do (exercise) Disease diagnosis (heart Are (ethnicity/assigned sex at birth) disease, osteoporosis) Possess (gene) Disease event (heart attack, Ingest (food, drug) fracture) 24  Case Control Study Have the Do not have 1 outcome 2 the outcome (cases) (controls) Were exposed Were not Were not Were exposed exposed exposed 3 3 25 Case Control Study Heart attack No heart 1 (cases) 2 attack (controls) Drank Caffeine No Caffeine Drank Caffeine No Caffeine 3 3 26 Measures of Association Undertake case-control studies to test a hypothesis (examine whether an exposure is associated with a disease) How do we know if there is an association? How do we know if the association is positive or negative? Use measures of association 27 Risk In epidemiology, a useful working definition of “risk” is the probability that an event will happen in the future We use this notation 𝐷+ 𝑅𝑖𝑠𝑘 = 𝐷 + 𝑝𝑙𝑢𝑠 𝐷 − Odds “odds” is the probability that an event will happen divided by the probability that it will not happen We use this notation 𝐷+ 𝑂𝑑𝑑𝑠 = 𝐷− Can we calculate risk in case-control? Risk in epidemiology is the probability that an event will occur at some point in the future In a case-control study, events have already happened, and the investigator picks the number of cases and controls, therefore, we cannot directly estimate “risk” because we cannot measure incidence However, we can calculate “odds”, which under many encountered epidemiological scenarios, is an acceptable approximation of risk 30 Prospective cohort study D+ (Heart attack= yes) E+ (caffeine= yes) D- (Heart attack = no) D+ (Heart attack = yes) E- (caffeine= no) D- (Heart attack = no) 31 Prospective - Follow Over Time Longitudinal/prospective/concurrent – Identify study population today and follow it up over time – “investigator … accompanies the subjects concurrently through calendar time…” 32 Issue with Risk Ratio Calculation of RR assumes that each person in a study has been followed-up for the same amount of time, and the whole study complete Often not so… – Drop-outs – Not everyone enters study at same time 33 What is person-time? Person-time is a way of correcting the problem (incidence density) Definition – The amount of follow-up time that each person contributes to a study – Computation begins on day person enters study – Computation ends on one of the following days: Last day for which data are available for persons who drop-out Day on which a person is diagnosed with the disease of interest Last day of study (last day of follow-up) 34 Calculating Person-Time (e.g., Person-Years) 35 www.europeanpublichealth.com Person-Years Incidence density (ID) = 4 cases/46 P-Ys ID = 0.09/1 P-Y or 9.0/100 P-Ys Incidence is 9.0 cases of disease per 100 person-years of follow-up 36 Incidence Density Ratio Calculate P-Ys separately for exposed and unexposed groups In exposed group (#1-5)… – ID = 2 cases/ 21 P-Ys In unexposed group (#6-10)… – ID = 2 cases/25 P-Ys IDR = IDexposed/IDunexposed = 2/21 P-Y = 0.095 IDR = 1.19 2/25 P-Y = 0.080 37 Attributable Risk “Excess” incidence due to exposure Exposed Unexposed 38 Attributable Risk: Smoking and CHD Smoking & CHD: – Risk of CHD in smokers = 0.028 – Risk of CHD in nonsmokers = 0.0174 Multiply by 1000 to get the following: – Risk in smokers = 28 per 1,000 – Risk in nonsmokers = 17.4 per 1,000 Let’s calculate attributable risk due to smoking 39 Attributable Risk: Smoking and CHD How much of the total risk in exposed persons is due to exposure (smoking)? In a cohort study everyone is free of disease at baseline: the 28 per 1,000 and 17.4 per 1,000/y are incidence rates AR = (incidence in exposed – incidence in unexposed) AR = (28.0 – 17.4)/1,000 = 10.6/1,000 40 Attributable Risk: Smoking and CHD 10.6 cases of CHD per 1,000 smokers are attributed to smoking If the smokers were to quit smoking, we would prevent 10.6 cases of CHD per 1,000 smokers 41 Bias sources of error that might provide an alternative explanation for the findings 42 Bias 1. Selection Bias 2. Information Bias 3. Confounding 43 Selection bias when the selection of subjects into a study or their likelihood of being retained in the study leads to a result that is different from what you would have gotten if you had enrolled the entire target population 44 Selection bias Cohort Studies Case-control studies Loss to follow-up Control selection Healthy worker effect Self-selection Non-response bias Differential surveillance Inclusion criteria Differential diagnosis Differential referral 45 Non-response bias People who we invite to participate and ignore our invitation are different from those who respond “yes” or “no” Mrs. Jones: “Good evening, Mrs. Jones. I am calling from TD bank, and would you have 15 minutes of your time to complete a survey?” “Why, I would love to, son.” Mr. de Souza: no answer Both de Souza and Jones selected at random to be part of the sampling frame; only Jones participates… does this bias the sample? Inclusion criteria Right-handed people will score differently on a test than left-handed people Bias may be introduced when evaluating the outcomes due to the way that the tests are performed Information bias A flaw in measurement of exposure, covariates, or outcomes that results in different quality (accuracy) of information between comparison groups 48 Information bias Cohort Studies Case-control studies Recall bias Recall bias Interviewer bias Interviewer bias Reporting bias Reporting bias Misclassification Misclassification Differential Differential Non-differential Non-differential 49 Misclassification = error Random Error 1. Reduces precision of the estimates (OR, RR, etc.) 2. Can reduce the amount of error by increasing sample size Systematic Error 1. Reduces validity or accuracy of the estimates 2. Accuracy = “getting the results right” (calculated OR or RR really represents what it happening) 3. Cannot reduce the amount of error by increasing sample size Differential Misclassification Measurements of outcome is different for exposed and unexposed subjects – Those with high BMI get a more sensitive test for heart disease than those with low BMI Measurement of exposure is different for diseased and non- diseased subjects – Those with heart disease have height and weight measured by a hospital scale; those without heart disease self-report from home Differential misclassification Research objective: to assess the association between exposure to pesticides and neurocognitive impairment, including fine motor coordination – Purdue Pegboard and MOART reaction time tests to measure the outcome – Both tests have separated evaluation of left and right hands, giving certain scoring to the performance. Recall Bias “Memory of exposure history is distorted by present health state” In a case-control study – Cases recall and/or report previous exposure differently from controls – Cases more likely to remember smoking – Controls more likely to forget smoking Recall bias in nutrition epidemiology Problems with FFQ Recall bias People forget what they ate (nondifferential misclassification) People with certain diseases (e.g., diabetes) more likely to remember what they ate (differential misclassification) Social desirability bias Underreporting of poor dietary habits or exaggeration of good dietary habits (nondifferential misclassification) Combatting Recall Bias If better recall of cases is because they are “searching” for a cause of the disease, use controls with a disease with a similar potential for the “search” Rely on hospital records Blind participants, study personnel to the hypothesis biomarkers “Randomized” Controlled Trials – avoiding bias Randomization ensures that, on average, the two groups (treatment and control) are similar in all respects except for the intervention The only difference between the “caffeine” group and the “no caffeine” group is the caffeine What is “controlled”? In an RCT, there are two groups Intervention/Treatment: “do something” Control/Comparison: “do nothing” Controlled means that there is a group who does not receive the intervention (caffeine) This group is called the “control” group (no caffeine) They approximate the “counterfactual” – what would have happened to those who drank caffeine had they not drank caffeine What is “trial”? A test of an unproven intervention or treatment for a set period of time Internal vs. External Validity – An RCT has internal validity when it is conducted in a methodologically sound manner Proper randomization Blinding (if possible) reduce unplanned crossovers, nonadherence Correct analysis of results Internal vs. External Validity External validity refers to how well the outcome of a study can be expected to apply to other settings How generalizable the findings are Do the findings apply to other people, settings, situations, and time periods? Minimizing Blinding – reducing “observer bias” ‘Double-blind’ Blinding of data collectors and data analysts Done to prevent knowledge of treatment from influencing how data are collected or analyzed ‘Triple-blind’ Blinding physicians and hospital staff who treat study subjects N-of-1 trial Patient receives the active and treatment creams in a randomly allocated sequence – Patient and physician are blinded to treatment sequence – At the end of the study period, results during the active treatment and placebo phases are compared to see if the active treatment benefitted the patient Benefit demonstrated → treat the patient No benefit demonstrated → don’t Vaccine efficacy Direct VE: Protection to those who received the vaccine Indirect VE: Protection to those who did not receive the vaccine Can be assessed by RCT as well as observational studies: (1 – OR) x 100 (1 – RR) x 100 Vaccine efficacy Flu Yes Flu No Total Vaccine Yes 2 198 200 Vaccine No 9 191 200 2/200 – 9/200 1- (2/9) 0.01 – 0.045 1- 0.2222 -0.035 =0.778 =77.8% -.0.035 / 0.045 -77.8% Systematic Reviews identify the best available evidence uses existing research evidence based on explicit systematic methods which can be used to make decisions  Metabolomics - Definition The term metabolome was first used in a landmark paper in 1998 and has grown into a science that studies the biochemical processes that involve metabolism. Metabolomics is the comprehensive analysis of small molecules/low molecular weight metabolites (